Graph and Sequential Neural Networks in Session-based Recommendation: A Survey

2024-08-27

ACM Computing Surveys

AI Summary

This paper addresses the insufficient modeling of short-term preferences in session-based recommendation (SBR). It systematically surveys and compares graph neural networks (GNNs) and sequential neural networks (RNNs/Transformers) in SBR, unifying their methodological evolution for the first time. The work clarifies task boundaries and establishes a structured taxonomy covering session graph construction, node representation learning, and interest aggregation. By analyzing shared principles and fundamental differences, it identifies key challenges—including dynamic interaction modeling and cross-session generalization—and outlines promising future research directions. The resulting framework provides an authoritative, reusable theoretical foundation and practical guidance for algorithm selection, improvement, and evaluation in SBR.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

Problem

• Surveying session-based recommendation systems' recent advancements
• Comparing sequential and graph neural networks in recommendation tasks
• Identifying challenges and future directions in session-based recommendations

Innovation

• Uses sequential neural networks for session-based recommendation
• Applies graph neural networks for dynamic preference capture
• Surveys standard frameworks and technical methods